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The fifth generation (5G) aims to connect massive amounts of devices with higher reliability, lower latency, and faster transmission speed, which are vital for implementing e-health systems. However, the current efforts on 5G e-health systems are still not enough to accomplish its full blueprint. In this article, we first discuss the related technologies from the physical layer, upper layer, and cross-layer perspectives on designing 5G e-health systems. We then elaborate two use cases according to our implementations (i.e., 5G e-health systems for remote health and 5G e-health systems for Covid-19 pandemic containment). We finally envision the future research trends and challenges of 5G e-health systems.

Recently, the Industrial Internet of Things (IIoT) is attracting growing attention from both academia and industry. Meanwhile, trust-based communication is widely utilized in various systems. In this article, studying the performance of IIoT, we investigate trust-based communication for IIoT. In particular, devoting attention to sensor- cloud, which is a paradigm of IIoT, we propose three types of trust-based communication mechanisms for sensor-cloud. Furthermore, with numerical results, we show that trust-based communication can greatly enhance the performance of sensor-cloud. Eventually, open research issues with respect to trust-based communication for sensor-cloud are discussed.

The emergence of the Industrial Internet of Things (IIoT) has paved the way to real-time big data storage, access, and processing in the cloud environment. In IIoT, the big data generated by various devices such as-smartphones, wireless body sensors, and smart meters will be on the order of zettabytes in the near future. Hence, relaying this huge amount of data to the remote cloud platform for further processing can lead to severe network congestion. This in turn will result in latency issues which affect the overall QoS for various applications in IIoT. To cope with these challenges, a recent paradigm shift in computing, popularly known as edge computing, has emerged. Edge computing can be viewed as a complement to cloud computing rather than as a competition. The cooperation and interplay among cloud and edge devices can help to reduce energy consumption in addition to maintaining the QoS for various applications in the IIoT environment. However, a large number of migrations among edge devices and cloud servers leads to congestion in the underlying networks. Hence, to handle this problem, SDN, a recent programmable and scalable network paradigm, has emerged as a viable solution. Keeping focus on all the aforementioned issues, in this article, an SDN-based edge-cloud interplay is presented to handle streaming big data in IIoT environment, wherein SDN provides an efficient middleware support. In the proposed solution, a multi-objective evolutionary algorithm using Tchebycheff decomposition for flow scheduling and routing in SDN is presented. The proposed scheme is evaluated with respect to two optimization objectives, that is, the trade-off between energy efficiency and latency, and the trade-off between energy efficiency and bandwidth. The results obtained prove the effectiveness of the proposed flow scheduling scheme in the IIoT environment.

With an exponential increase in smart device users, there is an increase in the bulk amount of data generation from various smart devices, which varies with respect to all the essential V's used to categorize it as big data. Generally, most service providers, including Google, Amazon, Microsoft and so on, have deployed a large number of geographically distributed data centers to process this huge amount of data generated from various smart devices so that users can get quick response time. For this purpose, Hadoop, and SPARK are widely used by these service providers for processing large datasets. However, less emphasis has been given on the underlying infrastructure (the network through which data flows), which is one of the most important components for successful implementation of any designed solution in this environment. In the worst case, due to heavy network traffic with respect to data migrations across different data centers, the underlying network infrastructure may not be able to transfer data packets from source to destination, resulting in performance degradation. Focusing on all these issues, in this article, we propose a novel SDN-based big data management approach with respect to the optimized network resource consumption such as network bandwidth and data storage units. We analyze various components at both the data and control planes that can enhance the optimized big data analytics across multiple cloud data centers. For example, we analyze the performance of the proposed solution using Bloom-filter-based insertion and deletion of an element in the flow table maintained at the OpenFlow controller, which makes most of the decisions for network traffic classification using the rule-and-action-based mechanism. Using the proposed solution, developers can deploy and analyze real-time traffic behavior for the future big data applications in MCE.

With the widespread popularity of Internet-enabled devices, there is an exponential increase in the information sharing among different geographically located smart devices. These smart devices may be heterogeneous in nature and may use different communication protocols for information sharing among themselves. Moreover, the data shared may also change with respect to various Vs (volume, velocity, variety, and value) to categorize it as big data. However, as these devices communicate with each other using an open channel, the Internet, there is a higher chance of information leakage during communication. Most of the existing solutions reported in the literature ignore these facts. Keeping focus on these points, in this article, we propose secure storage, verification, and auditing (SecSVA) of big data in cloud environment. SecSVA includes the following modules: an attribute-based secure data deduplication framework for data storage on the cloud, Kerberos-based identity verification and authentication, and Merkle hash-tree-based trusted third-party auditing on cloud. From the analysis, it is clear that SecSVA can provide secure third party auditing with integrity preservation across multiple domains in the cloud environment.

Data offloading using vehicles is one of the most challenging tasks to perform due to the high mobility of vehicles. There are many solutions available for this purpose, but due to the inefficient management of data along with the control decisions, these solutions are not adequate to provide data offloading by making use of the available networks. Moreover, with the advent of 5G and related technologies, there is a need to cope with high speed and traffic congestion in the existing infrastructure used for data offloading. Hence, to make intelligent decisions for data offloading, an SDN-based scheme is presented in this article. In the proposed scheme, an SDNbased controller is designed that makes decisions for data offloading by using the priority manager and load balancer. Using these two managers in SDN-based controllers, traffic routing is managed efficiently even with an increase in the size of the network. Moreover, a single-leader multi-follower Stackelberg game for network selection is also used for data offloading. The proposed scheme is evaluated with respect to several parameters where its performance was found to be superior in comparison to the existing schemes.

Nowadays, the study of underwater WSNs (UWSNs) has become a hot topic. However, UWSNs have not been fully utilized in the complex underwater environment, since there are some difficulties in controlling mobile sensor nodes and underwater environment conditions. In addition, how to ensure the security of UWSNs and the safety of underwater mobile sensor nodes has not been solved well. In this article, we study the problem of trust establishment between nodes in UWSNs. We first give a detailed overview of existing trust management mechanisms. Since UWSNs possess specific characteristics, it is noted that those existing mechanisms are not applicable for UWSNs. We then introduce a trust cloud model that is suitable for trust management in UWSNs.

With the widespread popularity and usage of ICT around the world, there is increasing interest in replacing the traditional electric grid by the smart grid in the near future. Many smart devices exist in the smart grid environment. These devices may share their data with one another using the ICT-based infrastructure. The analysis of the data generated from various smart devices in the smart grid environment is one of the most challenging tasks to be performed as it varies with respect to parameters such as size, volume, velocity, and variety. The output of the data analysis needs to be transferred to the end users using various networks and smart appliances. But sometimes networks may become overloaded during such data transmissions to various smart devices. Consequently, significant delays may be incurred, which affect the overall performance of any implemented solution in this environment. We investigate the use of VDTNs as one of the solutions for data dissemination to various devices in the smart grid environment using mobile edge computing. VDTNs use the store-and-carry forward mechanism for message dissemination to various smart devices so that delays can be reduced during overloading and congestion situations in the core networks. As vehicles have high mobility, we propose mobile edge network support assisted by the cloud environment to manage the handoff and the processing of large data sets generated by various smart devices in the smart grid environment. In the proposed architecture, most of the computation for making decisions about charging and discharging is done by mobile devices such as vehicles located at the edge of the network (also called mobile edge computing). The computing and communication aspects are explored to analyze the impact of mobile edge computing on performance metrics such as message transmission delay, response time, and throughput to the end users using vehicles as the mobile nodes. Our empirical results demonstrate an improved performance 10-15 percent increase in throughput, 20 percent decrease in response time, and 10 percent decrease in the delay incurred with our proposed solution compared to existing state-ofthe- art solutions in the literature.

Vehicular communications are emerging as a promising technology to enable communications using vehicles as network nodes. VDTNs appear as a novel approach to enable services and applications where it is not possible to define an end-to-end path. To allow communications in such challenging environments, VDTNs rely for their operation on cooperation between network nodes, which contributes to increasing network connectivity and improving overall network performance. To accomplish such a task, nodes may be asked to share their constrained storage, bandwidth, and energy resources with one another. However, nodes may be unwilling to cooperate in order to save resources or due to selfish behavior. This kind of node severely affects the network functionality. This article gives an overview of the field, providing motivations, challenges, and an evaluation of the impact of cooperative measures on the performance of VDTN networks.

In the large-scale petrochemical industry, one of the most concerning problems is the leakage of toxic gas. To solve this problem, it is necessary to locate the leak points and feed the possible location of leak points back to rescuers. Although some researchers have previously presented several methods to locate leak points, they ignored the impact of external factors, such as wind, and internal factors, such as the internal pressure of equipment, on the accurate detection of leak points. Fundamentally, both of those factors belong to context-aware data in a context-aware system. Therefore, this article proposes a context-aware system architecture for leak point detection in the large-scale petrochemical industry. In this three-layer architecture, a distributed database based on data categorization is designed in the storage layer, which is able to choose the most efficient approach to store the context-aware data from the gathering layer according to different context-aware data types. Then a real-time template matching algorithm for context-aware systems is presented in the computing layer to process the context-aware data stream. The architecture is a new scheme for accurate leak point detection, which is more consistent with practical application in the large scale petrochemical industry.

The five articles in this special section focus on new and enabling wireless technologies used in edge computing. With the advancements in the Internet of Things (IoT), billions of devices are expected to generate a huge amount of data that not only threaten to inundate communication networks but may also lead to ineffectiveness of conventional computing paradigms for analysis. The conventional computing approaches require transporting data from source devices over a network, storing it on centralized servers, and then performing analysis. The increase in volume elevates the complexity and costs of transporting and storing the data, and time required to analyze the data. To mitigate these issues, edge computing has been introduced, which aims to bring the cloud resources and services closer to data sources. The arrival of edge computing facilitates analyzing the IoT data closer to the sources, making applications more responsive to dramatically changing local conditions while avoiding communication bottlenecks of wide area networks. Several new wireless communication technologies are emerging to access the edge computing resources and services.

The twelve articles in this special section focus on energy efficiency as it relates to fog or edge computing. The Internet of Things (IoT) has emerged as one of the most advanced and complex technological trends, where more than 50 billion things will be connected (e.g., mobile devices, sensors, wearable devices, and other computing nodes) to the Internet by 2020. Edge/fog computing will play an increasingly important role in handling the information flow of such large and complex networks. An unintended consequence is the impact of their operations on carbon emissions and the resulting electricity costs. Thus, there has been focus on designing energy-efficient solutions for the edge-fog environment. In this Feature Topic, state-of-the-art research advances in energy efficiency and sustainability for edge/fog deployment are presented.

The nine articles in this special section focus on amateur drone surveillance system implementation issues such as software-defined radio solutions for amateur drone surveillance, context-aware amateur drone localization, and moving target surveillance based on a cooperative drone network. The drone can be the key enabler to provide high-speed wireless communication for fifth-generation systems. But its deployment poses several public safety threats to national institutions such as atomic plants, historical places, and presidential houses because it may carry explosive and destructive chemicals or surveillance equipment. To cope with these security threats, surveillance drone deployment is required for counter-surveillance of amateur drones. The main motivation of deploying surveillance drones is to keep an eye on the amateur drones, which may lead to serious disasters if no precautionary measures are taken. The Feature Topic articles in this issue highlight the most recent advances in amateur drone survelliance system implementation issues such as software- defined radio solutions for amateur drone surveillance, context-aware amateur drone localization, and moving target surveillance based on a cooperative drones network.

The Internet explosion makes energy consumption of the ICT sector a global concern. Nowadays, the power consumption of access networks increases rapidly due to the rollout of broadband services. Cable access networks provide Internet access over the existing cable television systems. Among all wired access networks, they have the second biggest user base, which is still growing fast due to the release of the new industry standard, DOCSIS 3.0. Therefore, we expect that cable access networks will play an important role in the ICT sector of future smart cities. In order to boost up bidirectional throughput, DOCSIS 3.0 introduces the channel bonding technology, which could lead to increased power consumption if not properly managed. In this article, we present a few energy-saving algorithms that could be used by the cable operators to improve the energy efficiency of cable access networks using channel bonding. The discussions cover both the network-wide energy-saving algorithm and the customer-side energy-saving algorithm, and provide investigations on the trade-offs between energy-saving and other network performance metrics, including packet delay and protocol overhead. Our numerical results suggest that effective energy saving can be achieved in wideband cable access networks with the proposed algorithms, and the packet delay and protocol overhead can be reduced if we choose the key parameters properly.

The current smart grid is undergoing a drastic change in order to deal with increasingly diversified and various service requests from the huge number of users. The next-generation smart grid, characterized by service-oriented middleware, will be upgraded by jointly employing the technologies in the areas of communications, control, and computing. To design a general middleware, an efficient design principle plays a fundamental role, while reliable communication infrastructure and heterogeneous applications lead to sustainability and stability. In this article, we propose an integrated and efficient middleware for heterogeneous services in a smart grid. Specifically, we first develop a mutual application access control principle that keeps users obtaining a satisfying assignment. Next, a collaborative and dynamic information exchange infrastructure is proposed for different smart meters, and a local information collector is designed to implement the communication and computing through power management messages. Finally, we present four steps to design a flexible service-oriented middleware for heterogeneous applications. Numerical simulation results are provided to demonstrate the efficiency of the proposed middleware.

The articles in this special section present the most recent advances in amateur drone surveillance network architecture and technologies. The advancement in communication, networking and sensing technologies has attracted researchers, hobbyists, and investors to deploy mini-drones, a.k.a. unmanned air vehicles (UAVs). Drones have boundless viable applications due to their small size and ability to fly without an on-board pilot such as in agriculture, surveillance, and numerous public services. The use of drones for achieving high speed wireless communication is one of the most significant applications for next-generation communication systems (5G). However, their deployment poses several public safety threats to national institutions such as atomic plants, historic places, and the homes of public officials because of their ability to carry explosives and other destructive chemicals. To cope with these security threats, the deployment of surveillance drones is required to monitor amateur drones. The main motivation of deploying surveillance drones is to keep an eye on amateur drones, which can cause serious disasters if no precautionary measures are taken on time. The increasing use of surveillance drones presents challenges such as robust detection, tracking, intruder localization, and jamming. The accuracy of detection is a basic requirement of the system. In general, accurate detection is time-consuming. In fact, a precise moving object detection method makes tracking more reliable and faster, and supports correct classification, which is quite important for surveillance drones to be successful.

Smart grid is a new emerging technology which is able to intelligently control the power consumption via network. Therefore, the efficiency of the information exchange between the power suppliers (or control centers) and power customers is an important issue for smart grid. Moreover, the performance of the smart grid usually depends on the customer's satisfaction degree which belongs to the field of quality of experience. In this article, we propose a QoE-driven power scheduling in the context of smart grid from the perspectives of architecture, strategy, and methodology. Specifically, it takes into account the QoE requirement when designing the power allocation scheme. For obtaining the QoE requirement, we analyze the fluctuation of the power load and the impact of the transmission delay. In particular, the power allocation is formulated as an optimization problem that maximizes the social welfare of the system. Based on the given QoE model, an efficient power scheduling scheme is proposed by jointly considering the admission control and QoE expectation. Extensive simulation results indicate that the proposed scheme can efficiently allocate the power according to the dynamic QoE requirements in a practical smart grid system.

This is the second part of the "Security and Privacy in Emerging Networks" Feature Topic. In Part I, which was published in April 2015, we selected those contributions that dealt with the theory behind the security and privacy of such networks. In Part II, we present articles that overview new security schemes for emerging networks such as vehicular, biomedical, underwater, crowdsourcing, and mobile networks. We feel that even though these emerging networks have attracted many research efforts lately, the security and privacy aspects have not been investigated well. Thus, it is important to provide ways to protect such networks from various security and privacy attacks. The aim of this FT is to promote further research interests in security and privacy in emerging networks by providing a vehicle for researchers and practitioners to discuss research challenges and open issues, and disseminate their latest research results. This can pave the way to implementing emerging networks with the necessary protection from major vulnerabilities. We received a large number of submissions but were obliged to accept only the best 13 papers. Part I was composed of six contributions that dealt with the theory of security/privacy threats, while this issue (Part II) is composed of seven articles addressing security challenges in a specific set of emerging networks.

This is the second part of the "Security and Privacy in Emerging Networks" Feature Topic. In Part I, which was published in April 2015, we selected those contributions that dealt with the theory behind the security and privacy of such networks. In Part II, we present articles that overview new security schemes for emerging networks such as vehicular, biomedical, underwater, crowdsourcing, and mobile networks. We feel that even though these emerging networks have attracted many research efforts lately, the security and privacy aspects have not been investigated well. Thus, it is important to provide ways to protect such networks from various security and privacy attacks. The aim of this FT is to promote further research interests in security and privacy in emerging networks by providing a vehicle for researchers and practitioners to discuss research challenges and open issues, and disseminate their latest research results. This can pave the way to implementing emerging networks with the necessary protection from major vulnerabilities. We received a large number of submissions but were obliged to accept only the best 13 papers. Part I was composed of six contributions that dealt with the theory of security/privacy threats, while this issue (Part II) is composed of seven articles addressing security challenges in a specific set of emerging networks.

Nowadays taking care of elderly people and the disabled has become a very important but challenging task. Although the elderly have wisdom and wealth gathered from their life experiences, they require special assistance, higher health insurance costs, and even constant monitoring. By utilizing information process and communications technology, ambient assisted living (AAL) communications open up a new way to address such needs for the aged and the sick. Specifically, AAL uses ambient technologies, including data sensing, processing, transmission, and artificial intelligence, to enable new products, services, and processes that help to provide safe, healthy lives for the aged and disabled. It also supports improved social connections and accessibility to the external world. With the growth of AAL environment, the accessibility gets more challenging for the complex data structure.